Overview

Dataset statistics

Number of variables23
Number of observations16209
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.8 MiB
Average record size in memory184.0 B

Variable types

Numeric18
DateTime1
Categorical4

Alerts

bathrooms is highly overall correlated with bedrooms and 6 other fieldsHigh correlation
bedrooms is highly overall correlated with bathrooms and 2 other fieldsHigh correlation
floors is highly overall correlated with bathrooms and 3 other fieldsHigh correlation
grade is highly overall correlated with bathrooms and 6 other fieldsHigh correlation
lat is highly overall correlated with zip_median_price and 1 other fieldsHigh correlation
long is highly overall correlated with zipcodeHigh correlation
price is highly overall correlated with grade and 4 other fieldsHigh correlation
sqft_above is highly overall correlated with bathrooms and 6 other fieldsHigh correlation
sqft_living is highly overall correlated with bathrooms and 5 other fieldsHigh correlation
sqft_living15 is highly overall correlated with bathrooms and 4 other fieldsHigh correlation
sqft_lot is highly overall correlated with sqft_lot15High correlation
sqft_lot15 is highly overall correlated with sqft_lotHigh correlation
view is highly overall correlated with waterfrontHigh correlation
waterfront is highly overall correlated with viewHigh correlation
yr_built is highly overall correlated with bathrooms and 2 other fieldsHigh correlation
zip_median_price is highly overall correlated with lat and 2 other fieldsHigh correlation
zip_tier is highly overall correlated with lat and 1 other fieldsHigh correlation
zipcode is highly overall correlated with longHigh correlation
waterfront is highly imbalanced (94.0%)Imbalance
view is highly imbalanced (72.1%)Imbalance
sqft_basement has 9882 (61.0%) zerosZeros
yr_renovated has 15537 (95.9%) zerosZeros

Reproduction

Analysis started2025-12-29 09:44:33.772293
Analysis finished2025-12-29 09:45:20.974681
Duration47.2 seconds
Software versionydata-profiling vv4.18.0
Download configurationconfig.json

Variables

id
Real number (ℝ)

Distinct16110
Distinct (%)99.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.5757708 × 109
Minimum1000102
Maximum9.9000002 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size126.8 KiB
2025-12-29T09:45:21.117838image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1000102
5-th percentile4.7500126 × 108
Q12.1230492 × 109
median3.9049502 × 109
Q37.304301 × 109
95-th percentile9.2943006 × 109
Maximum9.9000002 × 109
Range9.8990001 × 109
Interquartile range (IQR)5.1812518 × 109

Descriptive statistics

Standard deviation2.8746614 × 109
Coefficient of variation (CV)0.62823544
Kurtosis-1.2565548
Mean4.5757708 × 109
Median Absolute Deviation (MAD)2.3984503 × 109
Skewness0.24214742
Sum7.4168669 × 1013
Variance8.2636782 × 1018
MonotonicityNot monotonic
2025-12-29T09:45:21.316678image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15458002902
 
< 0.1%
40310005202
 
< 0.1%
35230690602
 
< 0.1%
19220592782
 
< 0.1%
26216000152
 
< 0.1%
43052000702
 
< 0.1%
81610200602
 
< 0.1%
14230490192
 
< 0.1%
51014056042
 
< 0.1%
27240492222
 
< 0.1%
Other values (16100)16189
99.9%
ValueCountFrequency (%)
10001021
< 0.1%
12000191
< 0.1%
12000211
< 0.1%
28000311
< 0.1%
36000571
< 0.1%
52000871
< 0.1%
72000801
< 0.1%
72001791
< 0.1%
74000621
< 0.1%
76000571
< 0.1%
ValueCountFrequency (%)
99000001901
< 0.1%
98950000401
< 0.1%
98423005401
< 0.1%
98423004851
< 0.1%
98393011651
< 0.1%
98393010551
< 0.1%
98393008751
< 0.1%
98393007751
< 0.1%
98393005451
< 0.1%
98393002851
< 0.1%

date
Date

Distinct366
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Memory size126.8 KiB
Minimum2014-05-02 00:00:00
Maximum2015-05-24 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-12-29T09:45:21.518243image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:45:21.714478image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

price
Real number (ℝ)

High correlation 

Distinct3428
Distinct (%)21.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean537470.28
Minimum75000
Maximum7700000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size126.8 KiB
2025-12-29T09:45:21.927973image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum75000
5-th percentile210000
Q1320000
median450000
Q3640000
95-th percentile1150000
Maximum7700000
Range7625000
Interquartile range (IQR)320000

Descriptive statistics

Standard deviation360303.58
Coefficient of variation (CV)0.6703693
Kurtosis37.106004
Mean537470.28
Median Absolute Deviation (MAD)150000
Skewness4.0330623
Sum8.7118558 × 109
Variance1.2981867 × 1011
MonotonicityNot monotonic
2025-12-29T09:45:22.151309image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
450000135
 
0.8%
350000134
 
0.8%
425000121
 
0.7%
550000113
 
0.7%
325000111
 
0.7%
375000109
 
0.7%
500000107
 
0.7%
400000106
 
0.7%
525000103
 
0.6%
250000100
 
0.6%
Other values (3418)15070
93.0%
ValueCountFrequency (%)
750001
 
< 0.1%
800001
 
< 0.1%
810001
 
< 0.1%
820001
 
< 0.1%
840001
 
< 0.1%
850002
< 0.1%
865001
 
< 0.1%
900004
< 0.1%
920001
 
< 0.1%
950004
< 0.1%
ValueCountFrequency (%)
77000001
< 0.1%
70625001
< 0.1%
68850001
< 0.1%
51108001
< 0.1%
46680001
< 0.1%
44890001
< 0.1%
42080001
< 0.1%
38000002
< 0.1%
37100001
< 0.1%
36350001
< 0.1%

bedrooms
Real number (ℝ)

High correlation 

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.3678203
Minimum0
Maximum33
Zeros8
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size126.8 KiB
2025-12-29T09:45:22.256794image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q13
median3
Q34
95-th percentile5
Maximum33
Range33
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.93327008
Coefficient of variation (CV)0.27711397
Kurtosis63.747881
Mean3.3678203
Median Absolute Deviation (MAD)1
Skewness2.4194036
Sum54589
Variance0.87099303
MonotonicityNot monotonic
2025-12-29T09:45:22.344716image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
37380
45.5%
45128
31.6%
22098
 
12.9%
51213
 
7.5%
6197
 
1.2%
1142
 
0.9%
726
 
0.2%
89
 
0.1%
08
 
< 0.1%
95
 
< 0.1%
Other values (2)3
 
< 0.1%
ValueCountFrequency (%)
08
 
< 0.1%
1142
 
0.9%
22098
 
12.9%
37380
45.5%
45128
31.6%
51213
 
7.5%
6197
 
1.2%
726
 
0.2%
89
 
0.1%
95
 
< 0.1%
ValueCountFrequency (%)
331
 
< 0.1%
102
 
< 0.1%
95
 
< 0.1%
89
 
0.1%
726
 
0.2%
6197
 
1.2%
51213
 
7.5%
45128
31.6%
37380
45.5%
22098
 
12.9%

bathrooms
Real number (ℝ)

High correlation 

Distinct29
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.1130545
Minimum0
Maximum8
Zeros7
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size126.8 KiB
2025-12-29T09:45:22.441473image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11.5
median2.25
Q32.5
95-th percentile3.5
Maximum8
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.76524195
Coefficient of variation (CV)0.36214966
Kurtosis1.0038592
Mean2.1130545
Median Absolute Deviation (MAD)0.5
Skewness0.46152496
Sum34250.5
Variance0.58559524
MonotonicityNot monotonic
2025-12-29T09:45:22.557547image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
2.54064
25.1%
12891
17.8%
1.752283
14.1%
2.251532
 
9.5%
21424
 
8.8%
1.51094
 
6.7%
2.75913
 
5.6%
3547
 
3.4%
3.5544
 
3.4%
3.25441
 
2.7%
Other values (19)476
 
2.9%
ValueCountFrequency (%)
07
 
< 0.1%
0.53
 
< 0.1%
0.7551
 
0.3%
12891
17.8%
1.258
 
< 0.1%
1.51094
 
6.7%
1.752283
14.1%
21424
 
8.8%
2.251532
 
9.5%
2.54064
25.1%
ValueCountFrequency (%)
81
 
< 0.1%
7.751
 
< 0.1%
6.751
 
< 0.1%
6.51
 
< 0.1%
6.251
 
< 0.1%
63
 
< 0.1%
5.752
 
< 0.1%
5.56
 
< 0.1%
5.2511
0.1%
517
0.1%

sqft_living
Real number (ℝ)

High correlation 

Distinct881
Distinct (%)5.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2073.2746
Minimum290
Maximum12050
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size126.8 KiB
2025-12-29T09:45:22.684981image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum290
5-th percentile940
Q11430
median1910
Q32550
95-th percentile3740
Maximum12050
Range11760
Interquartile range (IQR)1120

Descriptive statistics

Standard deviation907.00949
Coefficient of variation (CV)0.43747678
Kurtosis4.2573129
Mean2073.2746
Median Absolute Deviation (MAD)540
Skewness1.3787615
Sum33605708
Variance822666.22
MonotonicityNot monotonic
2025-12-29T09:45:22.815790image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1800103
 
0.6%
1400102
 
0.6%
1250101
 
0.6%
1300101
 
0.6%
101098
 
0.6%
154097
 
0.6%
172097
 
0.6%
144096
 
0.6%
182096
 
0.6%
165095
 
0.6%
Other values (871)15223
93.9%
ValueCountFrequency (%)
2901
< 0.1%
3701
< 0.1%
3801
< 0.1%
3902
< 0.1%
4202
< 0.1%
4301
< 0.1%
4401
< 0.1%
4601
< 0.1%
4702
< 0.1%
4802
< 0.1%
ValueCountFrequency (%)
120501
< 0.1%
100401
< 0.1%
98901
< 0.1%
96401
< 0.1%
80201
< 0.1%
80101
< 0.1%
78801
< 0.1%
78501
< 0.1%
77301
< 0.1%
74401
< 0.1%

sqft_lot
Real number (ℝ)

High correlation 

Distinct8048
Distinct (%)49.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14867.673
Minimum520
Maximum1164794
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size126.8 KiB
2025-12-29T09:45:22.943572image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum520
5-th percentile1755
Q15004
median7599
Q310631
95-th percentile43010.2
Maximum1164794
Range1164274
Interquartile range (IQR)5627

Descriptive statistics

Standard deviation38825.702
Coefficient of variation (CV)2.6114175
Kurtosis209.17359
Mean14867.673
Median Absolute Deviation (MAD)2616
Skewness11.407202
Sum2.4099011 × 108
Variance1.5074351 × 109
MonotonicityNot monotonic
2025-12-29T09:45:23.091437image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5000285
 
1.8%
6000197
 
1.2%
4000193
 
1.2%
7200165
 
1.0%
480092
 
0.6%
750090
 
0.6%
960089
 
0.5%
450082
 
0.5%
360080
 
0.5%
840078
 
0.5%
Other values (8038)14858
91.7%
ValueCountFrequency (%)
5201
< 0.1%
6091
< 0.1%
6381
< 0.1%
6492
< 0.1%
6511
< 0.1%
6751
< 0.1%
6761
< 0.1%
6811
< 0.1%
6831
< 0.1%
6901
< 0.1%
ValueCountFrequency (%)
11647941
< 0.1%
10742181
< 0.1%
10240681
< 0.1%
9829981
< 0.1%
9204231
< 0.1%
8712001
< 0.1%
7156901
< 0.1%
5336101
< 0.1%
5051661
< 0.1%
5039891
< 0.1%

floors
Real number (ℝ)

High correlation 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.4988278
Minimum1
Maximum3.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size126.8 KiB
2025-12-29T09:45:23.197835image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1.5
Q32
95-th percentile2
Maximum3.5
Range2.5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.54303211
Coefficient of variation (CV)0.36230453
Kurtosis-0.48039768
Mean1.4988278
Median Absolute Deviation (MAD)0.5
Skewness0.61531878
Sum24294.5
Variance0.29488387
MonotonicityNot monotonic
2025-12-29T09:45:23.277334image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
17970
49.2%
26215
38.3%
1.51414
 
8.7%
3489
 
3.0%
2.5117
 
0.7%
3.54
 
< 0.1%
ValueCountFrequency (%)
17970
49.2%
1.51414
 
8.7%
26215
38.3%
2.5117
 
0.7%
3489
 
3.0%
3.54
 
< 0.1%
ValueCountFrequency (%)
3.54
 
< 0.1%
3489
 
3.0%
2.5117
 
0.7%
26215
38.3%
1.51414
 
8.7%
17970
49.2%

waterfront
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size126.8 KiB
0
16096 
1
 
113

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters16209
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
016096
99.3%
1113
 
0.7%

Length

2025-12-29T09:45:23.374190image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-29T09:45:23.440114image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
016096
99.3%
1113
 
0.7%

Most occurring characters

ValueCountFrequency (%)
016096
99.3%
1113
 
0.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)16209
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
016096
99.3%
1113
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)16209
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
016096
99.3%
1113
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)16209
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
016096
99.3%
1113
 
0.7%

view
Categorical

High correlation  Imbalance 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size126.8 KiB
0
14604 
2
 
743
3
 
375
1
 
254
4
 
233

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters16209
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
014604
90.1%
2743
 
4.6%
3375
 
2.3%
1254
 
1.6%
4233
 
1.4%

Length

2025-12-29T09:45:23.524297image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-29T09:45:23.609150image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
014604
90.1%
2743
 
4.6%
3375
 
2.3%
1254
 
1.6%
4233
 
1.4%

Most occurring characters

ValueCountFrequency (%)
014604
90.1%
2743
 
4.6%
3375
 
2.3%
1254
 
1.6%
4233
 
1.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)16209
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
014604
90.1%
2743
 
4.6%
3375
 
2.3%
1254
 
1.6%
4233
 
1.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)16209
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
014604
90.1%
2743
 
4.6%
3375
 
2.3%
1254
 
1.6%
4233
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)16209
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
014604
90.1%
2743
 
4.6%
3375
 
2.3%
1254
 
1.6%
4233
 
1.4%

condition
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size126.8 KiB
3
10538 
4
4238 
5
1277 
2
 
131
1
 
25

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters16209
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row4
3rd row3
4th row3
5th row3

Common Values

ValueCountFrequency (%)
310538
65.0%
44238
26.1%
51277
 
7.9%
2131
 
0.8%
125
 
0.2%

Length

2025-12-29T09:45:23.707217image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-29T09:45:23.785373image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
310538
65.0%
44238
26.1%
51277
 
7.9%
2131
 
0.8%
125
 
0.2%

Most occurring characters

ValueCountFrequency (%)
310538
65.0%
44238
26.1%
51277
 
7.9%
2131
 
0.8%
125
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)16209
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
310538
65.0%
44238
26.1%
51277
 
7.9%
2131
 
0.8%
125
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)16209
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
310538
65.0%
44238
26.1%
51277
 
7.9%
2131
 
0.8%
125
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)16209
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
310538
65.0%
44238
26.1%
51277
 
7.9%
2131
 
0.8%
125
 
0.2%

grade
Real number (ℝ)

High correlation 

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.6529706
Minimum1
Maximum13
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size126.8 KiB
2025-12-29T09:45:23.860140image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6
Q17
median7
Q38
95-th percentile10
Maximum13
Range12
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1710497
Coefficient of variation (CV)0.15301897
Kurtosis1.2123456
Mean7.6529706
Median Absolute Deviation (MAD)1
Skewness0.75343493
Sum124047
Variance1.3713573
MonotonicityNot monotonic
2025-12-29T09:45:23.950322image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
76761
41.7%
84563
28.2%
91943
 
12.0%
61511
 
9.3%
10861
 
5.3%
11286
 
1.8%
5183
 
1.1%
1263
 
0.4%
424
 
0.1%
1310
 
0.1%
Other values (2)4
 
< 0.1%
ValueCountFrequency (%)
11
 
< 0.1%
33
 
< 0.1%
424
 
0.1%
5183
 
1.1%
61511
 
9.3%
76761
41.7%
84563
28.2%
91943
 
12.0%
10861
 
5.3%
11286
 
1.8%
ValueCountFrequency (%)
1310
 
0.1%
1263
 
0.4%
11286
 
1.8%
10861
 
5.3%
91943
 
12.0%
84563
28.2%
76761
41.7%
61511
 
9.3%
5183
 
1.1%
424
 
0.1%

sqft_above
Real number (ℝ)

High correlation 

Distinct803
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1784.7544
Minimum290
Maximum8860
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size126.8 KiB
2025-12-29T09:45:24.077486image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum290
5-th percentile850
Q11200
median1560
Q32200
95-th percentile3390
Maximum8860
Range8570
Interquartile range (IQR)1000

Descriptive statistics

Standard deviation821.82084
Coefficient of variation (CV)0.46046719
Kurtosis3.2764936
Mean1784.7544
Median Absolute Deviation (MAD)450
Skewness1.4303529
Sum28929084
Variance675389.5
MonotonicityNot monotonic
2025-12-29T09:45:24.203309image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1300158
 
1.0%
1010158
 
1.0%
1200151
 
0.9%
1220148
 
0.9%
1140140
 
0.9%
1340140
 
0.9%
1250140
 
0.9%
1400139
 
0.9%
1180133
 
0.8%
1320131
 
0.8%
Other values (793)14771
91.1%
ValueCountFrequency (%)
2901
< 0.1%
3701
< 0.1%
3801
< 0.1%
3902
< 0.1%
4202
< 0.1%
4301
< 0.1%
4401
< 0.1%
4601
< 0.1%
4702
< 0.1%
4802
< 0.1%
ValueCountFrequency (%)
88601
< 0.1%
85701
< 0.1%
80201
< 0.1%
78801
< 0.1%
78501
< 0.1%
76801
< 0.1%
74201
< 0.1%
73201
< 0.1%
66601
< 0.1%
64301
< 0.1%

sqft_basement
Real number (ℝ)

Zeros 

Distinct280
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean288.5202
Minimum0
Maximum4820
Zeros9882
Zeros (%)61.0%
Negative0
Negative (%)0.0%
Memory size126.8 KiB
2025-12-29T09:45:24.325847image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3560
95-th percentile1170
Maximum4820
Range4820
Interquartile range (IQR)560

Descriptive statistics

Standard deviation438.59891
Coefficient of variation (CV)1.5201671
Kurtosis2.6994341
Mean288.5202
Median Absolute Deviation (MAD)0
Skewness1.571497
Sum4676624
Variance192369
MonotonicityNot monotonic
2025-12-29T09:45:24.454213image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
09882
61.0%
600177
 
1.1%
700162
 
1.0%
800159
 
1.0%
500155
 
1.0%
400134
 
0.8%
1000120
 
0.7%
300110
 
0.7%
900107
 
0.7%
48084
 
0.5%
Other values (270)5119
31.6%
ValueCountFrequency (%)
09882
61.0%
102
 
< 0.1%
201
 
< 0.1%
402
 
< 0.1%
508
 
< 0.1%
609
 
0.1%
651
 
< 0.1%
706
 
< 0.1%
8014
 
0.1%
9016
 
0.1%
ValueCountFrequency (%)
48201
< 0.1%
35001
< 0.1%
34801
< 0.1%
32601
< 0.1%
28501
< 0.1%
27301
< 0.1%
26201
< 0.1%
26101
< 0.1%
26001
< 0.1%
25901
< 0.1%

yr_built
Real number (ℝ)

High correlation 

Distinct116
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1971.1528
Minimum1900
Maximum2015
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size126.8 KiB
2025-12-29T09:45:24.581663image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1900
5-th percentile1915
Q11952
median1975
Q31997
95-th percentile2011
Maximum2015
Range115
Interquartile range (IQR)45

Descriptive statistics

Standard deviation29.372698
Coefficient of variation (CV)0.01490128
Kurtosis-0.65458282
Mean1971.1528
Median Absolute Deviation (MAD)23
Skewness-0.47268095
Sum31950415
Variance862.75542
MonotonicityNot monotonic
2025-12-29T09:45:24.710181image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2014415
 
2.6%
2006347
 
2.1%
2005346
 
2.1%
2004330
 
2.0%
2007323
 
2.0%
2003321
 
2.0%
1977311
 
1.9%
1978290
 
1.8%
2008289
 
1.8%
1968268
 
1.7%
Other values (106)12969
80.0%
ValueCountFrequency (%)
190065
0.4%
190117
 
0.1%
190221
 
0.1%
190333
0.2%
190432
0.2%
190555
0.3%
190672
0.4%
190749
0.3%
190866
0.4%
190974
0.5%
ValueCountFrequency (%)
201530
 
0.2%
2014415
2.6%
2013144
 
0.9%
2012134
 
0.8%
201199
 
0.6%
2010116
 
0.7%
2009168
1.0%
2008289
1.8%
2007323
2.0%
2006347
2.1%

yr_renovated
Real number (ℝ)

Zeros 

Distinct69
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean82.738108
Minimum0
Maximum2015
Zeros15537
Zeros (%)95.9%
Negative0
Negative (%)0.0%
Memory size126.8 KiB
2025-12-29T09:45:24.838939image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum2015
Range2015
Interquartile range (IQR)0

Descriptive statistics

Standard deviation397.86115
Coefficient of variation (CV)4.8086807
Kurtosis19.175936
Mean82.738108
Median Absolute Deviation (MAD)0
Skewness4.6013062
Sum1341102
Variance158293.49
MonotonicityNot monotonic
2025-12-29T09:45:24.973133image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
015537
95.9%
201468
 
0.4%
200531
 
0.2%
200327
 
0.2%
200027
 
0.2%
200727
 
0.2%
201324
 
0.1%
200422
 
0.1%
200918
 
0.1%
199018
 
0.1%
Other values (59)410
 
2.5%
ValueCountFrequency (%)
015537
95.9%
19341
 
< 0.1%
19402
 
< 0.1%
19441
 
< 0.1%
19451
 
< 0.1%
19461
 
< 0.1%
19481
 
< 0.1%
19502
 
< 0.1%
19511
 
< 0.1%
19541
 
< 0.1%
ValueCountFrequency (%)
20158
 
< 0.1%
201468
0.4%
201324
 
0.1%
20127
 
< 0.1%
20118
 
< 0.1%
201013
 
0.1%
200918
 
0.1%
200816
 
0.1%
200727
 
0.2%
200615
 
0.1%

zipcode
Real number (ℝ)

High correlation 

Distinct70
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean98077.975
Minimum98001
Maximum98199
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size126.8 KiB
2025-12-29T09:45:25.120728image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum98001
5-th percentile98004
Q198033
median98065
Q398117
95-th percentile98177
Maximum98199
Range198
Interquartile range (IQR)84

Descriptive statistics

Standard deviation53.355282
Coefficient of variation (CV)0.0005440088
Kurtosis-0.84744549
Mean98077.975
Median Absolute Deviation (MAD)42
Skewness0.4029859
Sum1.5897459 × 109
Variance2846.7861
MonotonicityNot monotonic
2025-12-29T09:45:25.258961image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
98103458
 
2.8%
98038449
 
2.8%
98115437
 
2.7%
98117434
 
2.7%
98052429
 
2.6%
98042427
 
2.6%
98034389
 
2.4%
98006380
 
2.3%
98118376
 
2.3%
98133371
 
2.3%
Other values (60)12059
74.4%
ValueCountFrequency (%)
98001271
1.7%
98002151
 
0.9%
98003203
1.3%
98004235
1.4%
98005125
 
0.8%
98006380
2.3%
98007103
 
0.6%
98008207
1.3%
9801075
 
0.5%
98011152
 
0.9%
ValueCountFrequency (%)
98199233
1.4%
98198208
1.3%
98188103
 
0.6%
98178203
1.3%
98177185
1.1%
98168201
1.2%
98166190
1.2%
98155326
2.0%
9814837
 
0.2%
98146221
1.4%

lat
Real number (ℝ)

High correlation 

Distinct4775
Distinct (%)29.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47.560707
Minimum47.1593
Maximum47.7776
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size126.8 KiB
2025-12-29T09:45:25.388404image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum47.1593
5-th percentile47.31084
Q147.4725
median47.5724
Q347.6782
95-th percentile47.74986
Maximum47.7776
Range0.6183
Interquartile range (IQR)0.2057

Descriptive statistics

Standard deviation0.13833962
Coefficient of variation (CV)0.0029086956
Kurtosis-0.67433635
Mean47.560707
Median Absolute Deviation (MAD)0.1041
Skewness-0.48836173
Sum770911.49
Variance0.01913785
MonotonicityNot monotonic
2025-12-29T09:45:25.509447image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
47.540214
 
0.1%
47.696813
 
0.1%
47.662413
 
0.1%
47.671113
 
0.1%
47.68613
 
0.1%
47.565913
 
0.1%
47.665113
 
0.1%
47.675413
 
0.1%
47.684612
 
0.1%
47.685312
 
0.1%
Other values (4765)16080
99.2%
ValueCountFrequency (%)
47.15931
< 0.1%
47.16221
< 0.1%
47.16471
< 0.1%
47.17762
< 0.1%
47.18031
< 0.1%
47.18791
< 0.1%
47.18962
< 0.1%
47.192
< 0.1%
47.19031
< 0.1%
47.19132
< 0.1%
ValueCountFrequency (%)
47.77763
< 0.1%
47.77752
< 0.1%
47.77741
 
< 0.1%
47.77722
< 0.1%
47.77712
< 0.1%
47.7772
< 0.1%
47.77692
< 0.1%
47.77681
 
< 0.1%
47.77674
< 0.1%
47.77662
< 0.1%

long
Real number (ℝ)

High correlation 

Distinct708
Distinct (%)4.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-122.214
Minimum-122.519
Maximum-121.315
Zeros0
Zeros (%)0.0%
Negative16209
Negative (%)100.0%
Memory size126.8 KiB
2025-12-29T09:45:25.638393image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-122.519
5-th percentile-122.387
Q1-122.328
median-122.23
Q3-122.125
95-th percentile-121.979
Maximum-121.315
Range1.204
Interquartile range (IQR)0.203

Descriptive statistics

Standard deviation0.14009338
Coefficient of variation (CV)-0.0011462957
Kurtosis0.7645686
Mean-122.214
Median Absolute Deviation (MAD)0.101
Skewness0.83755585
Sum-1980966.8
Variance0.019626156
MonotonicityNot monotonic
2025-12-29T09:45:25.772620image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-122.2993
 
0.6%
-122.36284
 
0.5%
-122.28479
 
0.5%
-122.378
 
0.5%
-122.28877
 
0.5%
-122.29976
 
0.5%
-122.37276
 
0.5%
-122.35173
 
0.5%
-122.29173
 
0.5%
-122.29272
 
0.4%
Other values (698)15428
95.2%
ValueCountFrequency (%)
-122.5191
 
< 0.1%
-122.5141
 
< 0.1%
-122.5121
 
< 0.1%
-122.5112
< 0.1%
-122.5092
< 0.1%
-122.5061
 
< 0.1%
-122.5053
< 0.1%
-122.5042
< 0.1%
-122.5032
< 0.1%
-122.5021
 
< 0.1%
ValueCountFrequency (%)
-121.3151
< 0.1%
-121.3161
< 0.1%
-121.3522
< 0.1%
-121.4021
< 0.1%
-121.4031
< 0.1%
-121.4051
< 0.1%
-121.4171
< 0.1%
-121.4731
< 0.1%
-121.481
< 0.1%
-121.6911
< 0.1%

sqft_living15
Real number (ℝ)

High correlation 

Distinct692
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1983.1523
Minimum399
Maximum6210
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size126.8 KiB
2025-12-29T09:45:25.903419image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum399
5-th percentile1137.4
Q11480
median1840
Q32360
95-th percentile3290
Maximum6210
Range5811
Interquartile range (IQR)880

Descriptive statistics

Standard deviation681.90516
Coefficient of variation (CV)0.34384912
Kurtosis1.5794263
Mean1983.1523
Median Absolute Deviation (MAD)410
Skewness1.094927
Sum32144915
Variance464994.65
MonotonicityNot monotonic
2025-12-29T09:45:26.029315image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1560144
 
0.9%
1440144
 
0.9%
1540139
 
0.9%
1610131
 
0.8%
1500130
 
0.8%
1800125
 
0.8%
1680124
 
0.8%
1510124
 
0.8%
1480122
 
0.8%
1470122
 
0.8%
Other values (682)14904
91.9%
ValueCountFrequency (%)
3991
 
< 0.1%
4602
 
< 0.1%
6201
 
< 0.1%
6902
 
< 0.1%
7002
 
< 0.1%
7101
 
< 0.1%
7202
 
< 0.1%
7408
< 0.1%
7503
 
< 0.1%
7602
 
< 0.1%
ValueCountFrequency (%)
62101
 
< 0.1%
61101
 
< 0.1%
57904
< 0.1%
56101
 
< 0.1%
55001
 
< 0.1%
53801
 
< 0.1%
53401
 
< 0.1%
52201
 
< 0.1%
52001
 
< 0.1%
51101
 
< 0.1%

sqft_lot15
Real number (ℝ)

High correlation 

Distinct7279
Distinct (%)44.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12735.573
Minimum651
Maximum871200
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size126.8 KiB
2025-12-29T09:45:26.175172image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum651
5-th percentile1960
Q15098
median7620
Q310053
95-th percentile36939
Maximum871200
Range870549
Interquartile range (IQR)4955

Descriptive statistics

Standard deviation26933.162
Coefficient of variation (CV)2.1147979
Kurtosis123.53504
Mean12735.573
Median Absolute Deviation (MAD)2509
Skewness8.7516042
Sum2.064309 × 108
Variance7.2539522 × 108
MonotonicityNot monotonic
2025-12-29T09:45:26.306699image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5000328
 
2.0%
4000267
 
1.6%
6000213
 
1.3%
7200149
 
0.9%
7500107
 
0.7%
4800106
 
0.7%
840090
 
0.6%
360087
 
0.5%
510086
 
0.5%
408083
 
0.5%
Other values (7269)14693
90.6%
ValueCountFrequency (%)
6511
 
< 0.1%
7481
 
< 0.1%
7503
< 0.1%
7551
 
< 0.1%
7571
 
< 0.1%
7581
 
< 0.1%
7881
 
< 0.1%
7941
 
< 0.1%
8091
 
< 0.1%
8101
 
< 0.1%
ValueCountFrequency (%)
8712001
< 0.1%
4382131
< 0.1%
4347281
< 0.1%
4229671
< 0.1%
4119621
< 0.1%
3868121
< 0.1%
3802791
< 0.1%
3600001
< 0.1%
3393321
< 0.1%
3352891
< 0.1%

zip_median_price
Real number (ℝ)

High correlation 

Distinct67
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean485184.88
Minimum235000
Maximum1905000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size126.8 KiB
2025-12-29T09:45:26.432000image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum235000
5-th percentile259000
Q1332000
median445000
Q3575000
95-th percentile779500
Maximum1905000
Range1670000
Interquartile range (IQR)243000

Descriptive statistics

Standard deviation195753.45
Coefficient of variation (CV)0.40346156
Kurtosis6.436611
Mean485184.88
Median Absolute Deviation (MAD)130000
Skewness1.6480502
Sum7.8643617 × 109
Variance3.8319412 × 1010
MonotonicityNot monotonic
2025-12-29T09:45:26.556127image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
570000694
 
4.3%
575000505
 
3.1%
548500458
 
2.8%
339000449
 
2.8%
542500434
 
2.7%
620000429
 
2.6%
298000427
 
2.6%
445950389
 
2.4%
779500380
 
2.3%
361431376
 
2.3%
Other values (57)11668
72.0%
ValueCountFrequency (%)
235000352
2.2%
24900091
 
0.6%
258000271
1.7%
259000103
 
0.6%
263000203
1.3%
26500037
 
0.2%
266125208
1.3%
270000365
2.3%
275000172
1.1%
277554203
1.3%
ValueCountFrequency (%)
190500037
 
0.2%
1110000235
1.4%
997000204
1.3%
940000207
1.3%
779500380
2.3%
762450125
 
0.8%
740000142
 
0.9%
739999.5284
1.8%
71600081
 
0.5%
69000079
 
0.5%

zip_tier
Categorical

High correlation 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size126.8 KiB
3
3598 
1
3248 
2
3240 
4
3139 
5
2984 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters16209
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row4
5th row1

Common Values

ValueCountFrequency (%)
33598
22.2%
13248
20.0%
23240
20.0%
43139
19.4%
52984
18.4%

Length

2025-12-29T09:45:26.680238image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-29T09:45:26.766008image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
33598
22.2%
13248
20.0%
23240
20.0%
43139
19.4%
52984
18.4%

Most occurring characters

ValueCountFrequency (%)
33598
22.2%
13248
20.0%
23240
20.0%
43139
19.4%
52984
18.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)16209
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
33598
22.2%
13248
20.0%
23240
20.0%
43139
19.4%
52984
18.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)16209
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
33598
22.2%
13248
20.0%
23240
20.0%
43139
19.4%
52984
18.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)16209
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
33598
22.2%
13248
20.0%
23240
20.0%
43139
19.4%
52984
18.4%

Interactions

2025-12-29T09:45:18.195629image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:44:36.220876image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:44:40.485914image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:44:47.052346image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:44:48.903285image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:44:51.109845image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:44:52.973832image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:44:54.951764image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:44:57.698533image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:44:59.499815image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:45:01.262264image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:45:03.422122image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:45:05.310756image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:45:07.263669image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:45:10.391558image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:45:12.262775image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:45:14.103515image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:45:15.892386image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:45:18.293019image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:44:36.429893image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:44:40.698079image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:44:47.152854image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:44:49.009387image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:44:51.208671image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:44:53.075666image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:44:55.097492image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:44:57.795255image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:44:59.592908image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:45:01.352025image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:45:03.518924image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:45:05.418775image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:45:07.452390image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:45:10.509906image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:45:12.360724image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:45:14.202959image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:45:15.986038image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:45:18.405990image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:44:36.685334image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:44:40.938910image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:44:47.257135image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:44:49.116128image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:44:51.309137image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:44:53.180525image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:44:55.252595image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:44:57.897501image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:44:59.687236image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:45:01.456733image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:45:03.631872image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:45:05.524880image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:45:07.625292image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:45:10.616797image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:45:12.464837image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:45:14.302231image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:45:16.089656image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:45:18.512891image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:44:36.880974image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:44:41.521839image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:44:47.350908image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:44:49.219979image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:44:51.414965image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:44:53.282649image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:44:55.395415image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:44:58.008391image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:44:59.801665image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:45:01.902559image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:45:03.741339image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:45:05.627656image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:45:07.788014image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:45:10.719196image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:45:12.580466image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:45:14.398557image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:45:16.184479image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:45:18.624523image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:44:37.289887image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:44:42.037197image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:44:47.450871image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:44:49.327293image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:44:51.519951image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:44:53.388508image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:44:55.890559image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:44:58.113093image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:44:59.902806image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:45:02.008060image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:45:03.846743image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:45:05.739014image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:45:07.948722image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:45:10.828666image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:45:12.687186image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:45:14.495759image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:45:16.283656image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:45:18.730979image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:44:37.671105image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:44:42.622041image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:44:47.570648image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:44:49.431791image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:44:51.615537image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:44:53.494074image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:44:56.034476image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:44:58.217727image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:45:00.002105image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:45:02.112471image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:45:03.951853image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:45:05.847805image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:45:08.105038image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:45:10.931427image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:45:12.782053image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:45:14.601025image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:45:16.376677image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:45:18.845661image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:44:37.911898image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:44:43.294916image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:44:47.675269image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:44:49.537478image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:44:51.731425image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:44:53.596542image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:44:56.174670image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:44:58.316133image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:45:00.113662image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:45:02.227063image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:45:04.057321image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:45:05.952500image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:45:08.274860image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:45:11.031590image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:45:12.888374image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:45:14.703181image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:45:16.472097image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:45:18.944939image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:44:38.096742image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:44:43.469594image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:44:47.779317image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:44:49.651832image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:44:51.828781image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:44:53.709035image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:44:56.302711image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-12-29T09:45:13.085034image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:45:14.898106image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:45:16.668459image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:45:19.147478image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:44:38.436776image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:44:43.772186image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:44:47.974151image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:44:49.853858image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:44:52.020120image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:44:53.920549image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:44:56.595158image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:44:58.597898image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:45:00.384849image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:45:02.508873image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:45:04.373261image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:45:06.269973image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:45:08.719985image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:45:11.316588image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:45:13.179808image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:45:14.988537image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:45:16.765589image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:45:19.254079image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:44:38.623765image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:44:43.942358image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:44:48.068143image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:44:49.959644image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:44:52.123721image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:44:54.023211image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:44:56.736434image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:44:58.695935image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:45:00.474774image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:45:02.605178image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:45:04.470636image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:45:06.371920image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:45:08.876455image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:45:11.414005image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:45:13.279404image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:45:15.083544image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:45:16.858557image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:45:19.354991image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:44:38.838661image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:44:44.118656image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:44:48.174515image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:44:50.064309image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:44:52.231004image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:44:54.127711image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:44:56.895515image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:44:58.794056image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:45:00.570165image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:45:02.705202image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:45:04.572084image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:45:06.484760image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:45:09.619708image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:45:11.532508image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:45:13.381998image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:45:15.184129image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:45:16.957591image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:45:19.462782image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:44:39.068351image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:44:44.308723image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:44:48.285655image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:44:50.175992image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:44:52.341017image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:44:54.243302image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:44:57.073112image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:44:58.897030image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:45:00.671375image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:45:02.813800image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:45:04.678030image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:45:06.593982image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:45:09.746631image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:45:11.639549image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:45:13.488148image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:45:15.287832image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:45:17.060454image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:45:19.573233image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:44:39.240522image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:44:44.506049image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:44:48.397774image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:44:50.287868image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:44:52.453034image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:44:54.354342image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:44:57.219384image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:44:59.009779image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:45:00.772194image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:45:02.918801image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:45:04.790685image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:45:06.705892image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:45:09.855018image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:45:11.746526image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:45:13.610311image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:45:15.388986image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:45:17.168995image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:45:19.700286image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:44:39.423977image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:44:44.687472image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:44:48.498772image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:44:50.415740image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:44:52.556987image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:44:54.456572image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:44:57.319547image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:44:59.119889image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:45:00.868286image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:45:03.019682image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:45:04.889579image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:45:06.808599image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:45:09.962139image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:45:11.843511image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:45:13.711550image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:45:15.492793image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:45:17.269021image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:45:19.881244image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:44:39.658862image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:44:44.815426image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:44:48.605705image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:44:50.768436image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:44:52.665096image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:44:54.557496image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:44:57.414406image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:44:59.216472image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:45:00.963199image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:45:03.116736image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:45:04.989781image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:45:06.921852image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:45:10.068696image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:45:11.942239image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:45:13.811220image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:45:15.592162image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:45:17.364412image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:45:20.029410image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:44:39.867906image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:44:44.929707image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:44:48.706497image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:44:50.872553image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:44:52.774362image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:44:54.657232image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:44:57.509902image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:44:59.313260image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:45:01.056705image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:45:03.227021image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:45:05.089880image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:45:07.022548image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:45:10.174513image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:45:12.060809image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:45:13.910590image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:45:15.701276image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:45:17.461646image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:45:20.172906image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:44:40.143397image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:44:45.034494image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:44:48.797069image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:44:50.975979image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:44:52.867947image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:44:54.784824image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:44:57.603551image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:44:59.405275image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:45:01.165123image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:45:03.319873image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:45:05.191940image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:45:07.120570image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:45:10.281974image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:45:12.158856image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:45:14.003998image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:45:15.791157image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-29T09:45:17.553025image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-12-29T09:45:26.880995image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
bathroomsbedroomsconditionfloorsgradeidlatlongpricesqft_abovesqft_basementsqft_livingsqft_living15sqft_lotsqft_lot15viewwaterfrontyr_builtyr_renovatedzip_median_pricezip_tierzipcode
bathrooms1.0000.5240.1300.5510.6570.0170.0080.2570.4960.6930.1890.7450.5660.0640.0580.1090.1100.5630.0450.2110.121-0.203
bedrooms0.5241.0000.0210.2280.3770.011-0.0290.1870.3400.5390.2280.6450.4370.2090.1970.0340.0000.1760.0190.1010.072-0.171
condition0.1300.0211.0000.1820.1630.0330.0600.0840.0190.1050.0950.0610.0630.0310.0190.0270.0220.2500.0650.0460.0400.072
floors0.5510.2280.1821.0000.5030.0210.0260.1480.3180.597-0.2730.3980.299-0.240-0.2340.0170.0170.5600.0130.1710.106-0.063
grade0.6570.3770.1630.5031.0000.0240.1090.2230.6550.7060.0940.7120.6570.1490.1520.1350.0890.5010.0190.3760.205-0.181
id0.0170.0110.0330.0210.0241.000-0.0090.0090.0010.008-0.0020.0040.001-0.113-0.1120.0320.0000.035-0.0230.0090.094-0.006
lat0.008-0.0290.0600.0260.109-0.0091.000-0.1490.458-0.0290.1230.0310.031-0.124-0.1180.0710.028-0.1240.0280.6060.5140.248
long0.2570.1870.0840.1480.2230.009-0.1491.0000.0630.389-0.2070.2840.3860.3740.3780.0930.1050.413-0.0780.0980.251-0.579
price0.4960.3400.0190.3180.6550.0010.4580.0631.0000.5360.2550.6400.5700.0730.0610.2000.3090.0990.1060.7450.222-0.011
sqft_above0.6930.5390.1050.5970.7060.008-0.0290.3890.5361.000-0.1660.8430.6930.2690.2520.0810.0650.4700.0330.2030.129-0.284
sqft_basement0.1890.2280.095-0.2730.094-0.0020.123-0.2070.255-0.1661.0000.3290.1290.0370.0310.1540.146-0.1830.0630.1500.0790.116
sqft_living0.7450.6450.0610.3980.7120.0040.0310.2840.6400.8430.3291.0000.7430.3020.2830.1440.1430.3470.0530.2580.149-0.210
sqft_living150.5660.4370.0630.2990.6570.0010.0310.3860.5700.6930.1290.7431.0000.3630.3700.1480.0740.332-0.0020.3050.178-0.292
sqft_lot0.0640.2090.031-0.2400.149-0.113-0.1240.3740.0730.2690.0370.3020.3631.0000.9230.0340.035-0.0470.007-0.0610.032-0.321
sqft_lot150.0580.1970.019-0.2340.152-0.112-0.1180.3780.0610.2520.0310.2830.3700.9231.0000.0290.000-0.0260.008-0.0510.039-0.326
view0.1090.0340.0270.0170.1350.0320.0710.0930.2000.0810.1540.1440.1480.0340.0291.0000.5610.0430.1050.0600.0550.076
waterfront0.1100.0000.0220.0170.0890.0000.0280.1050.3090.0650.1460.1430.0740.0350.0000.5611.0000.0410.0880.0250.0280.078
yr_built0.5630.1760.2500.5600.5010.035-0.1240.4130.0990.470-0.1830.3470.332-0.047-0.0260.0430.0411.000-0.217-0.0010.120-0.312
yr_renovated0.0450.0190.0650.0130.019-0.0230.028-0.0780.1060.0330.0630.053-0.0020.0070.0080.1050.088-0.2171.0000.0620.0570.060
zip_median_price0.2110.1010.0460.1710.3760.0090.6060.0980.7450.2030.1500.2580.305-0.061-0.0510.0600.025-0.0010.0621.0000.686-0.023
zip_tier0.1210.0720.0400.1060.2050.0940.5140.2510.2220.1290.0790.1490.1780.0320.0390.0550.0280.1200.0570.6861.0000.384
zipcode-0.203-0.1710.072-0.063-0.181-0.0060.248-0.579-0.011-0.2840.116-0.210-0.292-0.321-0.3260.0760.078-0.3120.060-0.0230.3841.000

Missing values

2025-12-29T09:45:20.436428image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-12-29T09:45:20.758137image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

iddatepricebedroomsbathroomssqft_livingsqft_lotfloorswaterfrontviewconditiongradesqft_abovesqft_basementyr_builtyr_renovatedzipcodelatlongsqft_living15sqft_lot15zip_median_pricezip_tier
0911700017020150505T00000026864342.25181092402.0003718100196109805547.4362-122.18716609240298475.01
1670039021020140708T00000024500032.50160027882.0004716000199209803147.4034-122.18717203605290000.01
2721266054020150115T00000020000042.50172086382.0003817200199409800347.2704-122.31318707455263000.01
3856278020020150427T00000035249922.2512407052.00037115090200909802747.5321-122.0731240750575000.04
4776040035020141205T00000023200032.001280133561.0003712800199409804247.3715-122.07415908071298000.01
546400102520140918T00000072250043.50260051002.000381820780200309811747.6948-122.39520006720542500.03
6343250048620140623T00000029999521.00106072001.0004610600195109815547.7463-122.31518508291381500.02
7112605909520140526T00000088000032.002130351691.0004821300198909807247.7489-122.123286043560530000.03
8387650029020150305T00000017500031.00107061641.0003710700196709800147.3377-122.29113207920258000.01
9186540007520140522T00000032000032.259988442.00037798200200709811747.6983-122.3679981110542500.03
iddatepricebedroomsbathroomssqft_livingsqft_lotfloorswaterfrontviewconditiongradesqft_abovesqft_basementyr_builtyr_renovatedzipcodelatlongsqft_living15sqft_lot15zip_median_pricezip_tier
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